Prenatal levels of organophosphorus pesticide (OPs) biomarkers have been associated with hallmark symptoms of Attention Deficit Hyperactivity Disorder (ADHD), including deficits in working memory, social responsiveness, and ADHD indices1-4. Cross-sectional studies have also linked concurrent pyrethroid pesticide metabolites with ADHD and ADHD behaviors in children5-7. Toxicology studies report mixture effects for these pesticides on health outcomes8-10. Three major gaps exist in this literature: 1) No studies have evaluated prenatal and childhood exposures to these pesticides and clinical ADHD diagnoses. 2) Prenatal epidemiological studies of OPs/pyrethroids typically rely on urinary DAP biomarkers, which reflect ingestion of both non-toxic metabolites and toxic parent pesticides11. Urinary biomarkers from spot urine samples also do not characterize pregnancy-wide exposures12,13, since OPs and pyrethroids are rapidly metabolized14,15. 3) Studies of pesticide mixtures in epidemiology are scarce, and use of biomarkers for such mixtures analysis is problematic. For instance, administration of chlorpyrifos (an OP) results in increased tissue concentrations of cypermethrin while reducing urinary excretion of the pyrethroid metabolite 3-phenoxbenzoic acid16. Thus, the mixture itself may affect biomarker levels and increase exposure misclassification. A geospatial framework for pesticide exposure can address some of the limitations of urinary biomarkers: exposures from agricultural pesticide applications can be estimated for an entire pregnancy rather than a few days; estimates reflect the toxic parent pesticide rather than non-toxic OP metabolites; and estimates reflect actual exposures rather than a post-metabolism level. However, geospatial (GIS) methods of pesticide exposure assessment for epidemiology in the US have only been done in California17-20, and usually rely on distance-to-field measures. GIS exposure may be enhanced with drift models that incorporate heat, humidity, inversions, atmospheric stability, and wind21,22, while external validity may be increased by studying a population outside of California. We propose to assess the relationship between OPs, pyrethroids, and ADHD in an Arizona population. To identify a study population, we will apply a validated phenotyping algorithm with exceptional diagnostics23 to Arizona Medicaid records to identify 4,000 childhood ADHD cases and 16,000 controls. In the mentored phase, the Candidate will develop geospatial, phenotyping, exposure assessment, mixture modeling (Bayesian Kernel Machine regression [BKMR]), and machine learning skills while constructing the case-control study. In the R00 phase, the Candidate will compare the drift model against traditional distance-to-field measures in a frequentist framework (Aim 2), and model associations between prenatal OP and pyrethroid pesticide mixtures and ADHD with BKMR (Aim 3). These results will expand GIS studies beyond California, contribute to sparse but critical literature on pesticide mixtures and neurodevelopment, and be among the first to report associations between GIS estimates of prenatal pesticide exposures and ADHD case status.